Cost Uncertainty Research Articles

Optimal energy storage configuration to support 100 % renewable energy for Indonesia

This study presents a renewable energy (RE) optimization study to model the pathway to achieve 100 % carbon abatement, focussing on options for storage, using Indonesia's national electricity grid as a case study. Utilizing the PLEXOS energy simulation tool, the study covers the period 2021–2045. It employs an optimization of cost minimization function approach, encompassing investment, operation, maintenance, and unserved energy. The study integrates various components, including electricity supply and demand, transmission, renewable sources, and energy storage, while considering operational, build, and renewable energy target constraints. A range of scenarios are explored, varying in RE targets, battery capacities, and whether to include open-cycle gas turbines. The key novelty of this study is considering multiple versions of battery storage, with different options for the number of hours of storage. The findings indicate that higher RE targets lead to increased total installed nameplate capacity, with a significant portion from battery storage. In the early phases, batteries with 2-hour of capacity prioritized for short-term needs. As the focus shifts to more extended targets, batteries with a 4-hour capacity are recognized as more cost-effective and become the predominant choice. Over time, the least-cost strategy evolves to incorporate 10-hour capacity batteries to meet long-term energy storage requirements. To achieve a 100 % RE target by 2045, it is estimated that alongside every 100 MW of wind and solar capacity, there should be a corresponding 42 MW of energy storage. However, interpretations of these findings must consider the limited temporal resolution, uncertainties in demand and cost, and challenges related to grid inertia from energy storage, which may affect the stability and feasibility of the proposed solutions. This research offers crucial insights for energy policy and infrastructure development in renewable energy and storage system implementation.

Exploring cost performance tradeoffs and uncertainties for electric- and autonomous electric trucks using computational experiments

The recent development of battery electric trucks (BETs) suggests that they could play a vital role in transitioning to zero-emission road freight. To facilitate this transition, it is important to understand under which conditions BETs can be a viable alternative to internal combustion engine trucks (ICETs). Concurrently, the advancement of autonomous driving technology adds uncertainty and complexity to analyzing how the cost competitiveness of future zero-emissions trucks, such as autonomous electric trucks (AETs) may develop. This study examines the cost performance of BETs and AETs compared to ICETs, and how it varies over different market and technology conditions, charging strategies, and transport applications. Focus is on heavy-duty tractor-trailer trucks operating full truckload shuttle-flows in Sweden. Due to the inherent uncertainty and interactions among the analyzed factors, the analysis is performed as computational experiments using a simulation model of BET, AET, and ICET shuttle flow operations and associated costs. In total, 19,200 experiments are performed by sampling the model across 1200 scenarios representing various transport applications and technical and economic conditions for sixteen charging strategies with different combinations of depot, destination, and en route charging. The results indicate that both BETs and AETs are cost competitive compared to ICETs in a large share of scenarios. High asset utilization is important for offsetting additional investment costs in vehicles and chargers, highlighting the importance of deploying these vehicles in applications that enable high productivity. The cost performance for BETs is primarily influenced by energy related costs, charging strategy, and charging infrastructure utilization. The AET cost performance is in addition heavily affected by remote operations cost, and costs for the automated driving system. When feasible, relying only on depot charging is in many scenarios the most cost-effective charging strategy, with the primary exceptions being highly energy-demanding scenarios with long distances and heavy goods in which the required battery is too heavy to operate the truck within vehicle weight regulations if not complemented by destination, or en route charging. However, many experiments do not lead to a reduced payload capacity for BETs and AETs compared to ICETs, and a large majority of the considered scenarios are feasible to operate with a BET or AET within current gross vehicle weight regulations.

How Many Monte Carlo Samples are Needed for Probabilistic Cost-Effectiveness Analyses?

ObjectivesProbabilistic sensitivity analysis (PSA) is conducted to account for the uncertainty in cost and effect of decision options under consideration. PSA involves obtaining a large sample of input parameter values (N) to estimate the expected cost and effect of each alternative in the presence of parameter uncertainty. When the analysis involves using stochastic models (eg, individual-level models), the model is further replicated P times for each sampled parameter set. We study how N and P should be determined. MethodsWe show that PSA could be structured such that P can be an arbitrary number (say, P=1). To determine N, we derive a formula based on Chebyshev’s inequality such that the error in estimating the incremental cost-effectiveness ratio (ICER) of alternatives (or equivalently, the willingness-to-pay value at which the optimal decision option changes) is within a desired level of accuracy. We described 2 methods to confirm, visually and quantitatively, that the N informed by this method results in ICER estimates within the specified level of accuracy. ResultsWhen N is arbitrarily selected, the estimated ICERs could be substantially different from the true ICER (even as P increases), which could lead to misleading conclusions. Using a simple resource allocation model, we demonstrate that the proposed approach can minimize the potential for this error. ConclusionsThe number of parameter samples in probabilistic cost-effectiveness analyses should not be arbitrarily selected. We describe 3 methods to ensure that enough parameter samples are used in probabilistic cost-effectiveness analyses.

Ranking of factors affecting pricing in construction projects by intuitionistic fuzzy analytic hierarchy process with ordered pairs

The construction industry faces various challenges related to uncertainties in project bids and unstable pricing. These challenges include situations where uncertainties in bid documents can affect project pricing and performance. In order to resolve these uncertainties and clarify project requirements, risks and uncertainties within the document need to be identified early in the project life cycle. This study aims to rank the factors affecting project pricing. For this purpose, firstly, the parameters that have an impact on pricing are defined and then these factors are weighted by using Intuitionistic Fuzzy Analytic Hierarchy Process with Ordered Pairs. The analysis of factors affecting construction pricing reveals some important insights. Environmental uncertainties and uncertainties in prices and labor costs are the most important factors influencing pricing, while past project experience is the least important factor. Overall, these findings shed light on the multifaceted nature of construction pricing, which is shaped by the complex interaction of environmental, economic and market factors.

Collecting and Delivering Fattened Pigs to the Abattoir.

In the context of pig farming, this paper addresses the optimization problem of collecting fattened pigs from farms to deliver them to the abattoir. Assuming that the pig sector is organized as a competitive supply chain with narrow profit margins, our aim is to apply analytics to cope with the uncertainty in production costs and revenues. Motivated by a real-life case, the paper analyzes a rich Team Orienteering Problem (TOP) with a homogeneous fleet, stochastic demands, and maximum workload. After describing the problem and reviewing the related literature, we introduce the PJS heuristic. Our approach is first compared with exact methods, which are revealed as computationally unfeasible. Later, a scenario analysis based on a real instance was performed to gain insight into the practical aspects. Our findings demonstrate a positive correlation between the number of alternative routes explored, the number of trips, the transportation cost, and the maximum reward. Regarding the variability in the number of pigs to collect, when a truck can visit more than one farm, better solutions can be found with higher variability since the load can be combined more efficiently.

Multi-objective placement and sizing of energy hubs in energy networks considering generation and consumption uncertainties

This paper presents the placement and sizing of energy hubs (EHs) in electricity, gas, and heating networks. EH is a coordinator framework for various power sources, storage devices, and responsive loads. For simultaneous modeling of economic, operation, reliability, and flexibility indices, the proposed scheme is expressed as a three-objective optimization in the form of Pareto optimization based on the sum of weighted functions. The objective functions of this problem respectively minimize the planning cost of EHs (equal to the total cost of construction of hubs and their expected operating cost), the expected energy loss of the mentioned networks, and the expected energy not-supplied (EENS) of these networks in the case of an N − 1 event. The problem is constrained by power flow equations and operation and reliability constraints of these network together with the EH planning and operation model, and flexibility constraints of the EHs. Then, to achieve unique optimal solution in the shortest possible time, a linear approximation model is extracted for the proposed scheme. Moreover, scenario-based stochastic programming (SBSP) is employed to model uncertainties of load, energy cost, renewable power, and accessibility of the mentioned network equipment. Finally, the obtained numerical results indicate the capability of the proposed scheme in enhancing the economic and flexibility situation of EHs and improving the reliability and operation status of energy networks along with achieving optimal planning and operation for EHs.

Overcoming Long Inference Time of Nearest Neighbors Analysis in Regression and Uncertainty Prediction

The intuitive approach of comparing like with like, forms the basis of the so-called nearest neighbor analysis, which is central to many machine learning algorithms. Nearest neighbor analysis is easy to interpret, analyze, and reason about. It is widely used in advanced techniques such as uncertainty estimation in regression models, as well as the renowned k-nearest neighbor-based algorithms. Nevertheless, its high inference time complexity, which is dataset size dependent even in the case of its faster approximated version, restricts its applications and can considerably inflate the application cost. In this paper, we address the problem of high inference time complexity. By using gradient-boosted regression trees as a predictor of the labels obtained from nearest neighbor analysis, we demonstrate a significant increase in inference speed, improving by several orders of magnitude. We validate the effectiveness of our approach on a real-world European Car Pricing Dataset with approximately 4.2×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$4.2 \ imes 10^6$$\\end{document} rows for both residual cost and price uncertainty prediction. Moreover, we assess our method’s performance on the most commonly used tabular benchmark datasets to demonstrate its scalability. The link is to github repository where the code is available: https://github.com/koutefra/uncertainty_experiments.

Robust Consensus Modeling: Concerning Consensus Fairness and Efficiency with Uncertain Costs

Extensive uncertainty can affect the efficiency and fairness of consensus in the consensus reaching process (CRP), but few scholars have studied consensus modeling that focuses on fairness and efficiency in uncertain environments. Additionally, the weight of the decision maker (DM) in the CRP is influenced by multiple factors. Therefore, this paper proposes robust consensus models (EFCMs) focusing on fairness and efficiency under uncertain costs to address these issues. Firstly, this paper constructs multiple uncertainty sets to describe the uncertainty of the unit adjustment cost. Secondly, the fair utility level and opinion adjustment distance are used to measure the fairness and efficiency of reaching consensus, respectively. Furthermore, this paper uses a data-driven method based on the KDE method combined with trust propagation in social networks to determine the DMs’ weights jointly. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between the government and enterprises. The experimental results and sensitivity analysis show that the consensus cost budget and the DMs’ jealous preference behavior particularly affects the efficiency of reaching consensus, which provides a theoretical basis for solving practical decision making problems.

Performance and cost potential for direct-fired supercritical CO2 natural gas power plants

Direct-fired supercritical CO2 (sCO2) power cycles are being explored as an attractive alternative to natural gas combined cycle (NGCC) plants with carbon capture and storage (CCS). Therefore, understanding their performance and cost potential is important for the commercialization of the technology. This study presents detailed techno-economic optimization results of natural gas-fired, utility-scale power plants based on the direct sCO2 power cycle, which is lacking in public literature. The study considered multiple plant configurations with varying levels of thermal integration with the plant air separation unit (ASU) to shows the systematic impact of thermal integration on plant performance and economics. Several design variables for each power cycle configuration were identified and optimized to minimize the levelized cost of electricity (LCOE). The optimized direct sCO2 power plants achieved ∼48 % plant efficiency (HHV basis) which is similar or slightly higher plant efficiencies than state-of-the-art NGCC plants based on the F-class gas turbine with carbon capture and storage (CCS). The LCOE of the optimized direct sCO2 plants is 13–17 % higher than the reference NGCC plants with CCS due to high capital costs associated with the ASU and sCO2 power block, though there is significant room for improvement due to high uncertainty in component capital costs for these new plants. Based on the results, direct sCO2 power cycles have the potential to achieve >50 % efficiency (HHV basis) with >98 % CO2 capture rate which can lead to significant efficiency improvement and CO2 emissions reductions compared to NGCC plants with CCS.

Multistage scenario-based planning for locating of pharmaceutic distribution centers for emergency patients by considering practical constraints under budget uncertainty conditions (case study: Kerman city in Iran)

In the realm of healthcare, medicine and the pharmaceutical supply chain exert notable influence. Recognized as a strategic commodity, any disruption in its supply chain can precipitate significant crises. The dispersed nature of pharmacies and the critical need for prompt medication delivery to patients underscore the paramount importance of designing an optimal pharmaceutical supply chain. This necessity is particularly pronounced when addressing the needs of diabetic and epilepsy patients, who, being more susceptible and in urgent need of medication, intensify the significance of a robust supply chain. Given the critical role of medicine and the pharmaceutical supply chain in healthcare, the repercussions of supply chain disruptions are far-reaching. A well-designed pharmaceutic supply chain is indispensable, especially in ensuring timely access to medications for vulnerable patient groups. To address this imperative, this study proposes a multi-stage scenario-based mathematical model aimed at enhancing the responsiveness of demand regions amidst uncertain budgets and feasible scenarios for meeting the insulin and phenobarbital requirements of patients. By incorporating uncertainties in both demand and procurement costs, the practicability of applying this model in real-world contexts has been strengthened. A case study focusing on Kerman City in Iran is conducted to evaluate the efficacy of the proposed model.

Comparative study on the organic rankine cycle off-design performance under different zeotropic mixture flow boiling correlations

Zeotropic mixture utilization enhances the performance of the Organic Rankine cycle (ORC) significantly. For geothermal ORC power plants, the zeotropic mixture flow boiling correlation plays a vital role in both evaporator design and system performance evaluation. Based on the screened zeotropic mixture R1224yd(Z)/R1234ze(E) (0.452/0.548), a comparative analysis of 4 flow boiling correlations is performed to assess their impact on both evaporator design and system performance under off-design conditions. The results underscore a substantial impact of these correlations on determining the plate length, with the largest relative difference being 53.8%, consequently resulting in uncertainties of 31.7% and 23.4% for the evaporator exergy loss and cost, respectively. Under off-design working conditions, the relative difference in net power output varies from 16.43% to 7.67%. The exergy efficiency variation is up to 11.82% and the electricity production cost (EPC) uncertainty is up to 11.2% at the working condition with the maximum difference in net output work. This study provides valuable insights for selecting the appropriate zeotropic mixture flow boiling correlation in practical evaporator design and evaluating the off-design performance of ORC systems.

Ensemble learning for predicting average thermal extraction load of a hydrothermal geothermal field: A case study in Guanzhong Basin, China

Accurate prediction of the average thermal extraction load (ATEL) in hydrothermal heating systems optimizes energy recovery, though numerical models are constrained by modeling accuracy and computational time costs. Data-driven modeling can efficiently screen alternative production plans and enhance daily operational decisions. However, ensemble learning applications for forecasting ATEL lack comprehensive comparisons and appropriate algorithm selection. This study evaluates six ensemble learning algorithms: random forest (RF), adaptive boosting (AdaBoost), gradient boosting decision trees (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightBGM) and categorical boosting (CatBoost) on 1159 data points from a homogeneous thermal-hydraulic coupling numerical model. The superior performance of the CatBoost is characterized by the highest accuracy (with the lowest root mean square error (RMSE) of 150.6 kW–170.7 kW, minimal mean absolute percentage error (MAPE) of 0.66%–0.73%, and exceptional R2 values of 0.999), commendable stability, reasonable computational cost and low uncertainty. LightGBM, XGBoost, and GBDT are identified as suitable substitutes, whereas RF is a possible substitute. Moreover, the production rate is identified as the most significant factor contributing to the ATEL, followed by the well spacing and injection temperature. While perforation depth has the least influence on heat recovery.

A multi-criteria decision-making framework for sustainable road transport systems: Integrating stakeholder-cost-environment-energy for a highway case study in United Arab Emirates

Existing lifecycle assessment models narrowly concentrate on limited parameters, disregarding system ecology and multi-stage stakeholder integration with sequestered analyses. Assimilation of new transport technologies with traditional design-bid-build decision-making paradigm should acknowledge sustainability triple-bottom-line social, cost, and environmental impacts, across all lifecycle stages for a system-wide optimisation. This study presents a multilevel framework to evaluate road transport system projects utilising 1) stakeholder engagement for flexibility in establishing key performance indicators, 2) passenger surveys to identify what encourages mode choice towards public transport, 3) microsimulation models, lifecycle cost and lifecycle analysis for quantitative assessment, 4) analytic hierarchy for qualitative assessment, and finally 5) multi-criteria modelling with stakeholder expert group preferences. Framework is applied to a 5-lane dual carriageway project in Abu Dhabi, analysing eight virgin and recycled material-based and public transport service alternatives. Study revealed the significance of saved user time, reduced fuel consumption during use phase and vehicle operational costs as the highest lifecycle impact generators. The total sustainability performance across the lifecycle for all alternatives was affected by traffic gridlock formation due to traffic growth in later years and lack of traffic load reduction strategies. Results showed that an autonomous vehicle-based bus rapid transit service coupled with recycled material to construct the road significantly reduced lifecycle cost (51.1%), energy (55.6%) and pollutant (54.5%-CO2, 49.7%-NOx, 24.1%-PM) burdens. It exhibited effectiveness of system choice using lifecycle assessment over the typically fragmented project planning and evaluation approach. Overall, framework exhibited robustness against unit cost and LCI uncertainties, fuel distribution assumptions, discount rate, and indicator weights variations based on priorities of different stakeholder groups.

Towards Risk-Aware Real-Time Security Constrained Economic Dispatch: A Tailored Deep Reinforcement Learning Approach

In the presence of increasing uncertainties brought by intermittent renewable energy sources, security and risk management continue to be the most critical concern in modern power system operation. Risk-aware, real-time security constrained economic dispatch (RT-SCED) provides an efficient solution towards promptly, economically and robustly responding to the changes in the power system operating state. Despite different model-based methods have been developed to handle uncertainties, significant computation burden arise to incorporate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -1 contingency constraints with a higher temporal resolution in RT-SCED. Driven by similar computational challenges, risk evaluation is often overlooked in the current application of deep reinforcement learning (DRL) based data-driven methods in RT-SCED. This paper proposes a DRL-based risk-aware RT-SCED methodological framework by incorporating a novel data-driven risk evaluation model to foster efficient agent-environment interactions. The real-time dispatch policies are constructed with an improved twin delayed deep deterministic policy gradient method. The policy network features a residual network architecture and incorporates an active power allocation mechanism to integrate empirical dispatch knowledge, preventing early termination and fostering more efficient learning behavior. Case studies validate the superior performance of the proposed method in risk-aware RT-SCED on cost efficiency, uncertainty adaptability and computational efficiency, through benchmarking against model-based and data-driven baseline methods.

Robust maximum expert consensus model with adjustment path under uncertain environment

The maximum expert consensus model (MECM) is a commonly used consensus model in group decision making (GDM). In traditional MECM, the consensus constraints are not fully considered and the adjustment cost of decision maker (DM) is certain. Moreover, directing the DM’s opinion in a visual path is seldom considered in the consensus reaching process (CRP) of MECM. Inspired by these issues, this paper first proposed two MECMs with different types of consensus constraints. Then, this paper incorporated an adjustment path into MECM by using feedback coefficients that can prevent opinions from being overadjusted. Furthermore, the robust MECM (RMECM) is developed to address the uncertainty of unit adjustment cost under three uncertainty sets. Finally, the feasibility of the proposed models is verified by applying them to the allocation of special funds in the Chinese film industry, which is a large-scale group decision making (LSGDM) problem. The sensitivity analysis and comparative analysis are also conducted to show the efficiency of the proposed models.

Operational cost uncertainty and financial performance of manufacturing firms in Kenya

The main objective of this study was to find out if uncertainty about the operational costs of manufacturing firms in Kenya affects financial performance. Kenyan manufacturing firms have not been performing as expected. They are meant to contribute to economic growth through GDP increments and market share, attract the largest strategic investments in the key processing industry, increase sales locally and internationally, and employ 20% of the Kenyan population. However, manufacturing firms have been facing various financial and non-financial challenges, including declining profit and sales, and some firms have moved out of the market. Many factors have been cited as contributing to declining financial performance. However, the influence of operational cost uncertainty on the financial performance of manufacturing firms in Kenya is not conclusive. Some studies found a negative relationship, while others found a positive relationship. Hence, the current study necessitates examining the influence of firm operational cost uncertainty on the financial performance of manufacturing firms in Kenya. The study anchored its variable on agency theory, which states that during financial uncertainty, operational costs are volatile. Indicators of operational cost uncertainty were the labour cost ratio and the research and development ratio, and proxies of performance were ROS and ROE. The study adopted positivism, philosophy, and an explanatory design. The target population was 856 manufacturing firms registered with the Kenya Association of Manufacturers. A sample of 90 firms was drawn from the population using the Nasuirma (2000) formula. The stratified random sampling technique was applied to 14 sectors, and each sample was picked by random sampling. The study covered 12 years, starting from 2009 to 2020. Panel data was collected from audited financial statements using a data collection instrument. Results showed that operational cost uncertainty had a positive and significant influence on the financial performance of manufacturing firms in Kenya. The labour cost ratio had a negative and significant relationship with the financial performance of manufacturing firms in Kenya. The research and development ratio had a positive and significant impact on the financial performance of manufacturing firms in Kenya. The study recommends manufacturing firms have enhanced technology in place to help reduce production costs. The study recommends having enhanced research and development in place that will take advantage of the market niche for products and technology for production.

Hybrid data-driven and physics-based simulation technique for seismic analysis of steel structural systems

This paper proposes 1) a new hybrid analysis technique by integrating a data-driven method with a physics-based technique to perform nonlinear analysis of steel structural systems under seismic loading, 2) two component-based data-driven models (PI-SINDy and DPI-SINDy) for predicting the nonlinear hysteretic response of steel seismic fuses with and without hysteretic degradation. The proposed hybrid data-driven and physics-based simulation (HyDPS) technique offers an efficient approach for seismic analysis of structures and is expected to address the challenges associated with computational cost and modeling uncertainties inherent in physics-based numerical simulations. In this technique, the well-understood components of the structure modeled numerically are combined with the critical components of the structure simulated using one of the data-driven models developed in this study. The proposed data-driven models were trained using experimental and numerical hysteresis data. The results show that these data-driven models can accurately and efficiently predict the nonlinear hysteretic response of steel structural components with and without degradation. Furthermore, the performance of the HyDPS technique powered by the PI-SINDy model is verified in the presence of noise using response history analyses performed on a steel buckling-restrained braced frame.

Whether and When to Invest in Transportation Projects: Combining Scenarios and Real Options to Manage the Uncertainty of Costs and Benefits

Transportation infrastructure projects are a cornerstone of economic growth. However, the issue of whether new transportation infrastructure projects deliver the expected benefits has come under considerable scrutiny. The growing economic uncertainty and the tightening of budget constraints have made the design, evaluation, and selection of such high-cost projects particularly critical. There are disagreements as to how project decision-makers can evaluate the long-term costs and benefits of infrastructure projects. The objective of this article is to address such disagreements. We develop and apply an innovative methodological approach that combines real options with scenarios to help policymakers assess the costs and benefits of transportation projects. While these techniques have been widely adopted in corporations, there is little empirical evidence regarding their combined use by project decision-makers dealing with complex infrastructure projects. In this article, we fill this gap in the planning and project studies literature. We show that scenarios and real options can be very helpful in developing a more comprehensive understanding of long-term impacts of major infrastructure projects and thus in selecting the most relevant projects. Overall, our article assists the debate on the management of the uncertainty of long-term costs and benefits of infrastructure projects and helps cope with such uncertainty.

Cost Uncertainty, Financial Aid, and the Enrollment Choices of Low-Income Students

Cost Uncertainty, Financial Aid, and the Enrollment Choices of Low-Income Students

Homo Heuristicus: From Risk Management to Managing Uncertainty in Large-Scale Infrastructure Projects

Large-scale infrastructure projects tend to experience variances between estimated and final costs. Governments, in response, have been using statistical methods (including probabilistic approaches) such as reference class forecasting to try and mitigate cost overruns. While helpful in accommodating risk, such statistical methods often fail to account for a project's cost uncertainty. To reduce cost variance under uncertainty, it is necessary to use heuristics when formulating an infrastructure project's cost contingency. This article argues a need to adopt a vision of human nature described as Homo heuristicus (heuristics-using-person). We provide awareness and rationale for developing an “adaptive toolbox” of heuristics for ecologically rational and contextually aligned cost contingency decision-making. However, the selection, recognition, and evaluation heuristics for determining cost uncertainty have yet to be examined. Thus, future research is required to cultivate the heuristics needed to address the uncertainty that surrounds the determination of a cost contingency.